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On universal transfer learning [PDF]
Theoretical Computer Science, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Proceedings of the 2012 SIAM International Conference on Data Mining, 2012 Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the target domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applications. The general idea of the existing methods is to utilize the common latent structure shared across domains as the ...
Mingsheng Long +5 more
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Factors influencing the learning transfer of nursing students in a non-face-to-face educational environment during the COVID-19 pandemic in Korea: a cross-sectional study using structural equation modeling [PDF]
Journal of Educational Evaluation for Health Professions, 2023 Purpose The aim of this study was to identify factors influencing the learning transfer of nursing students in a non-face-to-face educational environment through structural equation modeling and suggest ways to improve the transfer of learning.
Geun Myun Kim +2 more
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Fuzzy Inference and Manifold Regularization Combined Feature Transfer Learning
Jisuanji kexue yu tansuo, 2020 Transfer learning leverages the rich data in the source domain to provide support for building accurate models in the target domain. Feature transfer learning is a kind of widely studied technology in transfer learning, but the existing feature transfer ...
SONG Yixuan, DENG Zhaohong, QIN Bin
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Komputika, 2023 Deep learning is a branch of machine learning with many highly successful applications. One application of deep learning is image classification using the Convolutional Neural Network (CNN) algorithm. Large image data is required to classify images with
Muhammad Daffa Arviano Putra +4 more
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Artificial Intelligence, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peilin Zhao +3 more
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CoRR, 2020 We provide visualizations of individual neurons of a deep image recognition network during the temporal process of transfer learning. These visualizations qualitatively demonstrate various novel properties of the transfer learning process regarding the speed and characteristics of adaptation, neuron reuse, spatial scale of the represented image ...
Róbert Szabó +4 more
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Bayesian Transfer Learning. [PDF]
Stat SciTransfer learning is a burgeoning concept in statistical machine learning that seeks to improve inference and/or predictive accuracy on a domain of interest by leveraging data from related domains. While the term "transfer learning" has garnered much recent interest, its foundational principles have existed for years under various guises.
Suder PM, Xu J, Dunson DB.
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Frontiers in Psychology, 2017 The current experiment investigated generalizability of motor learning in proximal versus distal effectors in upper extremities. Twenty-eight participants were divided into three groups: training proximal effectors, training distal effectors, and no ...
Tore K. Aune +3 more
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Deep Transfer Learning for Biology Cross-Domain Image Classification
Journal of Control Science and Engineering, 2021 Automatic biology image classification is essential for biodiversity conservation and ecological study. Recently, due to the record-shattering performance, deep convolutional neural networks (DCNNs) have been used more often in biology image ...
Chunfeng Guo, Bin Wei, Kun Yu
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